

"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase   :	Any												=						logging.get_logger(__name__)  # pylint: disable=invalid-name
class      _UpperCAmelCase   (							_lowerCAmelCase       ):



	def __init__(  self     :       List[Any]			,    _lowercase     :       List[str]			,    _lowercase     :       Optional[Any]						):
		super().__init__()
		self.register_modules(unet=_lowercase			,    scheduler=_lowercase						)



	@torch.no_grad()
	def __call__(  self     :       Any			,    _lowercase     :       int = 1			,    _lowercase     :       int = 1_00			,    _lowercase     :       Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    _lowercase     :       Optional[float] = None			,    _lowercase     :       bool = True			,    ):

		if audio_length_in_s is None:
			__UpperCAmelCase      		= self.unet.config.sample_size / self.unet.config.sample_rate

		__UpperCAmelCase      		= audio_length_in_s * self.unet.config.sample_rate

		__UpperCAmelCase      		= 2 ** len(self.unet.up_blocks						)
		if sample_size < 3 * down_scale_factor:
			raise ValueError(
			    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
			    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''						)

		__UpperCAmelCase      		= int(_lowercase						)
		if sample_size % down_scale_factor != 0:
			__UpperCAmelCase      		= (
			    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
			) * down_scale_factor
			logger.info(
			    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
			    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
			    ''' process.'''						)
		__UpperCAmelCase      		= int(_lowercase						)

		__UpperCAmelCase      		= next(iter(self.unet.parameters()						)						).dtype
		__UpperCAmelCase      		= (batch_size, self.unet.config.in_channels, sample_size)
		if isinstance(_lowercase			,    _lowercase						) and len(_lowercase						) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(_lowercase						)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

		__UpperCAmelCase      		= randn_tensor(_lowercase			,    generator=_lowercase			,    device=self.device			,    dtype=_lowercase						)

		# set step values
		self.scheduler.set_timesteps(_lowercase			,    device=audio.device						)
		__UpperCAmelCase      		= self.scheduler.timesteps.to(_lowercase						)

		for t in self.progress_bar(self.scheduler.timesteps						):
			# 1. predict noise model_output
			__UpperCAmelCase      		= self.unet(_lowercase			,    _lowercase						).sample

			# 2. compute previous image: x_t -> t_t-1
			__UpperCAmelCase      		= self.scheduler.step(_lowercase			,    _lowercase			,    _lowercase						).prev_sample

		__UpperCAmelCase      		= audio.clamp(-1			,    1						).float().cpu().numpy()

		__UpperCAmelCase      		= audio[:, :, :original_sample_size]

		if not return_dict:
			return (audio,)

		return AudioPipelineOutput(audios=_lowercase						)




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  List[Any]				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Any				  =						{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Dict       =    """gpt_bigcode"""
							A_				: str       =    ["""past_key_values"""]
							A_				: Tuple       =    {
							    """hidden_size""": """n_embd""",
							    """max_position_embeddings""": """n_positions""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(       self       ,   __snake_case=5_0257       ,   __snake_case=1024       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=None       ,   __snake_case="gelu_pytorch_tanh"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=1e-5       ,   __snake_case=0.02       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=5_0256       ,   __snake_case=5_0256       ,   __snake_case=True       ,   __snake_case=True       ,   __snake_case=True       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= n_positions
											_SCREAMING_SNAKE_CASE					:			List[Any]							= n_embd
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= n_layer
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= n_head
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= n_inner
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= activation_function
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= resid_pdrop
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= embd_pdrop
											_SCREAMING_SNAKE_CASE					:			List[Any]							= attn_pdrop
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= layer_norm_epsilon
											_SCREAMING_SNAKE_CASE					:			Any							= initializer_range
											_SCREAMING_SNAKE_CASE					:			str							= scale_attn_weights
											_SCREAMING_SNAKE_CASE					:			Tuple							= use_cache
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= attention_softmax_in_fpaa
											_SCREAMING_SNAKE_CASE					:			List[Any]							= scale_attention_softmax_in_fpaa
											_SCREAMING_SNAKE_CASE					:			Any							= multi_query

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= bos_token_id
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= eos_token_id

											super().__init__(bos_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   **__snake_case    )




